1. Technical Field
The present invention relates to a deterministic component model identifying apparatus, an identifying method, a program, a recording medium, a test system, and an electronic device.
2. Related Art
Conventionally, electronic circuits, communication systems and the like may be evaluated based on measurement of characteristic values of electrical signals and the like. In the field of serial communication, for example, communication systems may be evaluated by measuring jitter contained in transmission or reception signals.
A characteristic value such as jitter is divided into deterministic and random components. The deterministic components are deterministically generated according to signal patterns and characteristics of transmission paths and the like, and the random components are randomly generated. To achieve relatively detailed evaluation, the deterministic and random components are preferably separated from each other.
Deterministic and random components may be separately measured by measuring a characteristic value multiple times and thus creating a histogram (also referred to as a probability density function). Conventionally, random components are separated from the resultant histogram in such a manner that the left and right tails of the histogram are approximated by a random distribution (a Gaussian distribution). Furthermore, the deterministic components are separated from the resultant histogram in such a manner that an interval between mean values of the two random components generated by the approximation is calculated as a peak-to-peak value of the deterministic components.
Here, the conventional separating method assumes that deterministic components contained in a histogram have a dual-Dirac model. It should be noted, however, that there are other deterministic component models such as a sinusoidal distribution and a uniform distribution. Therefore, the conventional separating method has difficulties in accurately separating jitter into deterministic and random components when a histogram contains other deterministic component models than the dual-Dirac model. For example, as shown in FIG. 2, when a histogram contains deterministic components having a sinusoidal distribution, the conventional separating method yields a peak-to-peak value DJ(δ-δ) for the deterministic components, which is smaller than a true value DJP-P.